Synthetic Temperature Data for P-Flash - A Machine Learning-Based Model for Flashover Prediction Using Recovered Temperature Data
This data set provides heat detector temperatures in a single story three-compartment structure. 1000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various t-squared fires. The peak heat release rate and time to peak range from approximately 50 kW to 2200 kW and from 50 s to 1400 s, respectively. A detailed description of this work can be found in Ref. [2]. [1] Tam, W.C., Fu, E.Y., Peacock, R., Reneke, P., Wang, J., Li, J. and Cleary, T., 2020. Generating synthetic sensor data to facilitate machine learning paradigm for prediction of building fire hazard. Fire Technology, pp.1-22. [2] Wang, J., Tam, W.C., Jia, Y., Peacock, R., Reneke, P., Fu, E.Y. and Cleary, T., 2021. P-Flash - A machine learning-based model for flashover prediction using recovered temperature data. Fire Safety Journal, 122, p.103341.
Resources
3 resources available
-
DOI Access for Synthetic temperature data for development of a machine learning based flashover prediction model
FILE -
Heat Detector Temperature Data and Supplemental Material
EXCEL SPREADSHEET WITH 4 TABS -
SHA256 File for Heat Detector Temperature Data and Supplemental Material
TEXT/PLAIN
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[ "006:55" ] |
| contactPoint |
{ "fn": "Andy Tam", "hasEmail": "mailto:waicheong.tam@nist.gov" } |
| description | This data set provides heat detector temperatures in a single story three-compartment structure. 1000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various t-squared fires. The peak heat release rate and time to peak range from approximately 50 kW to 2200 kW and from 50 s to 1400 s, respectively. A detailed description of this work can be found in Ref. [2]. [1] Tam, W.C., Fu, E.Y., Peacock, R., Reneke, P., Wang, J., Li, J. and Cleary, T., 2020. Generating synthetic sensor data to facilitate machine learning paradigm for prediction of building fire hazard. Fire Technology, pp.1-22. [2] Wang, J., Tam, W.C., Jia, Y., Peacock, R., Reneke, P., Fu, E.Y. and Cleary, T., 2021. P-Flash - A machine learning-based model for flashover prediction using recovered temperature data. Fire Safety Journal, 122, p.103341. |
| distribution |
[ { "title": "DOI Access for Synthetic temperature data for development of a machine learning based flashover prediction model", "accessURL": "https://doi.org/10.18434/M32258" }, { "title": "Heat Detector Temperature Data and Supplemental Material", "format": "Excel spreadsheet with 4 tabs", "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "description": "Schematic of simulation setup, list of simulation runs and conditions, specification of heat detectors, and temperature data of all heat detectors from 1000 simulation runs.", "downloadURL": "https://data.nist.gov/od/ds/mds2-2258/P-Flash%20all%20data%202020-6-26.xlsx" }, { "title": "SHA256 File for Heat Detector Temperature Data and Supplemental Material", "mediaType": "text/plain", "downloadURL": "https://data.nist.gov/od/ds/mds2-2258/P-Flash%20all%20data%202020-6-26.xlsx.sha256" } ] |
| identifier | ark:/88434/mds2-2258 |
| issued | 2020-09-04 |
| keyword |
[ "Machine learning; Synthetic temperature data; Flashover occurrence prediction; Smart firefighting" ] |
| landingPage | https://data.nist.gov/od/id/mds2-2258 |
| language |
[ "en" ] |
| license | https://www.nist.gov/open/license |
| modified | 2020-03-25 00:00:00 |
| programCode |
[ "006:045" ] |
| publisher |
{ "name": "National Institute of Standards and Technology", "@type": "org:Organization" } |
| references |
[ "https://link.springer.com/article/10.1007/s10694-020-01022-9", "https://www.sciencedirect.com/science/article/pii/S0379711221000825" ] |
| theme |
[ "Fire:Fire detection", "Fire:Fire fighting" ] |
| title | Synthetic Temperature Data for P-Flash - A Machine Learning-Based Model for Flashover Prediction Using Recovered Temperature Data |
